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Record W3019396609 · doi:10.1177/0164027520920024

Genes-in-Dyads: A Study of Relationship Quality

2020· article· en· W3019396609 on OpenAlexaff
Aniruddha Das

Bibliographic record

VenueResearch on Aging · 2020
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsMcGill University
FundersNational Institute on Aging
KeywordsTraitHealth and Retirement StudyPsychologyPartner effectsInterpersonal communicationExternalityQuality of life (healthcare)Life course approachSocial psychologyInterpersonal relationshipDevelopmental psychologyBiologyDemographySociologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: Using dyadic genetic information on older couples, this study queried associations of a polygenic score for well-being with one's own as well as a partner's relationship experiences. METHOD: Data were from the 2010 wave of the U.S. Health and Retirement Study. Analysis was through structural equation modeling. RESULTS: Especially among women, the genetic score was associated with individuals' own relationship experiences. Genetic externalities-linkages of one's genes with a partner's experiences-were also observed. No significant gender variations emerged. DISCUSSION: Contrary to conceptions implicit in much of existing genetics literature-which focuses on individuals' own gene-trait associations-the interpersonal environments most crucial to life course and health outcomes are shaped by the genes of all involved actors. Genetic externalities are a central component. Implications for the life course and gene-environment literatures are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.680
GPT teacher head0.591
Teacher spread0.089 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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